Three-stage reject inference learning framework for credit scoring using unsupervised transfer learning and three-way decision theory

• Published in: Decision Support Systems 2020-10, Vol.137, p.113366, Article 113366
• Main Authors: Shen, Feng, Zhao, Xingchao, Kou, Gang
• Format: Article
• Language: English
• Subjects: Credit scoring; Decision theory; Machine learning; Reject inference; Risk management; Statistical inference; Statistical methods; Three-way decision theory; Unsupervised transfer learning
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2020.113366

There has been significant research into reject inference, with several statistical methods and machine learning techniques having been employed to infer the possible repayment behavior of rejected credit applicants. This study proposes a novel three-stage reject inference learning framework using unsupervised transfer learning and three-way decision theory that integrates: (1) the rejected credit sample selection using three-way decision theory, (2) higher-level representations to transfer learning from both accepted and selected rejected credit samples; and (3) credit scoring using the reconstructed accepted credit samples. This method was found to both perform well for reject inference and handle negative transfer learning problems. The numerical results were validated on Chinese credit data, the results from which demonstrated the superiority of the proposed reject inference method for credit risk management applications. •Three-stage reject inference learning framework based on transfer learning was proposed for credit scoring.•The proposed three-stage learning framework effectively addressed the negative transfer problem.•We validated the proposed learning framework by Chinese credit data.•The proposed learning framework significantly enhanced credit scoring performance.•The proposed method outperformed classical reject inference models.